Administrative Supplement to Support Collaborations to Improve AIML-Readiness of NIH-Supported Data for Parent Award SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer

支持合作的行政补充，以提高 NIH 支持的家长奖数据的 AIML 就绪性 SCH：头部自适应放射治疗的个性化重新安排

• 批准号: 10594327
• 负责人: Clifton David Fuller
• 金额: $ 32.04万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594327
• 关键词: Acute; Administrative Supplement; Adverse event; Aftercare; Agreement; Artificial Intelligence; Award; Barium swallow; Benchmarking; Collaborations; Collection; Common Terminology Criteria for Adverse Events; Communities; Complication; Consensus; Data; Data Pooling; Data Set; Databases; Deglutition; Deposition; Development; Digital Imaging and Communications in Medicine; Dose; Enteral Feeding; Equipment and supply inventories; Frequencies; Functional disorder; Funding; Head Cancer; Head and neck structure; Image; Individual; Information Dissemination; Institution; Label; Machine Learning; Magnetic Resonance Imaging; Manuscripts; Measures; Medical Imaging; Modeling; Neck Cancer; Nomenclature; Normal tissue morphology; Ontology; Organ; Outcome; Parents; Pathologic; Patient Outcomes Assessments; Patients; Physicians; Physics; Prevalence; Probability; Procedures; Process; Protocols documentation; Publications; Radiation Oncology; Radiation therapy; Radiology Specialty; Readiness; Registries; Reporting; Research; Risk; Roentgen Rays; Serial Magnetic Resonance Imaging; Statistical Models; Symptoms; Testing; The Cancer Imaging Archive; Therapeutic; Time; Toxic effect; Tumor Tissue; United States National Institutes of Health; Validation; automated segmentation; base; cancer imaging; cancer therapy; cohort; crowdsourcing; data curation; data integrity; data repository; design; experience; head and neck cancer patient; imaging Segmentation; improved; interest; large-scale database; learning community; machine learning model; medical attention; parent grant; personalized medicine; predicting response; predictive modeling; prospective; radiomics; repository; response; serial imaging; therapy outcome; treatment planning; treatment response; tumor

Project Summary We have collected, under our parent award (1R01CA257814-01), a database of serial multi-parametric magnetic resonance (MR) images as well as patient-reported and objective toxicity measures for more than 400 head and neck (HNC) patients, at pre-, on-, and post-therapy. We plan to utilize this data, in complete alignment with the first specific aim of the parent award, to effectively quantify treatment-related response on tumor/node and normal tissue in order to develop personalized treatment planning adaptations for individual HNC patients. As the most data-rich image toxicity cohort to the best of our knowledge, however, this database necessitates rigorous curation to be utilized for artificial intelligence/machine learning (AI/ML) approaches to predict, for example, tumor complication probability (TCP) and normal tissue complication probability (NTCP). Specifically, multi-observer segmentation of tumor and normal tissue regions of interest is required. Additionally, dissemination efforts are necessary to engage experts from AI/ML communities to develop AI/ML-approaches for auto-segmentation models, and TCP/NTCP predictions. To this end, we plan to undertake three specific aims. Through our first specific aim, we plan to curate our serial multi-parametric, multi time-point MRI dataset (accompanied with extracted radiomics) for therapeutic response and TCP prediction through assembling a team of three physicians to obtain the ground-truth segmented images. We further plan to deposit the curated segmented images as a dataset to The Cancer Imaging Archive (TCIA). As our second specific aim, we plan for curation and public deposition of matched image-dose multi-time-point acute and late toxicity metrics to be disseminated to both AI/ML experts for NTCP modeling. We will particularly include patient-reported MD Anderson Symptom Inventory-Head and Neck (MDASI-HN) toxicity outcomes, Common Toxicity Criteria- Adverse Events (CTC-AE) physician-ranked toxicity, and objective measures of swallowing dysfunction such as modified barium swallowing and tube-feeding assessments. In the third specific aim, we plan to design and execute a public crowdsourced challenge for serial image dose- response prediction for both TCP and NTCP prediction modeling tasks. Based on the test dataset that we plan to release after the execution of the challenge, we will conduct a post-challenge analysis on the submitted models (e.g., false-positive, and false-negative cases), and disseminate the best results as manuscripts to be submitted for publications and presentations. If successful, the proposed efforts are directly responsive to the need for AI/ML-ready datasets to be utilized for cancer treatment.

项目总结